* mark Geometry as experimental

* install QtAlignedMalloc
* finish the renaming Regression->LeastSquares
* install LeastSquares directory (!!!)
* misc dox fixes

Eigen/Array

@@ -7,7 +7,7 @@
 
 namespace Eigen {
 
-/** \defgroup Array Array module
+/** \defgroup Array_Module Array module
   * This module provides several handy features to manipulate matrices as simple array of values.
   * In addition to listed classes, it defines various methods of the Cwise interface
   * (accessible from MatrixBase::cwise()), including:

Eigen/CMakeLists.txt

@@ -1,4 +1,4 @@
-set(Eigen_HEADERS Core LU Cholesky QR Geometry Sparse Array SVD LeastSquares StdVector)
+set(Eigen_HEADERS Core LU Cholesky QR Geometry Sparse Array SVD LeastSquares QtAlignedMalloc StdVector)
 
 if(EIGEN_BUILD_LIB)
     set(Eigen_SRCS

Eigen/Geometry

@@ -14,7 +14,10 @@
 
 namespace Eigen {
 
-/** \defgroup GeometryModule Geometry module
+/** \defgroup Geometry_Module Geometry module
+  *
+  * \nonstableyet
+  *
   * This module provides support for:
   *  - fixed-size homogeneous transformations
   *  - translation, scaling, 2D and 3D rotations

Eigen/LeastSquares

@@ -11,15 +11,15 @@
 
 namespace Eigen {
 
-/** \defgroup Regression_Module Regression module
+/** \defgroup LeastSquares_Module LeastSquares module
   * This module provides linear regression and related features.
   *
   * \code
-  * #include <Eigen/Regression>
+  * #include <Eigen/LeastSquares>
   * \endcode
   */
 
-#include "src/Regression/Regression.h"
+#include "src/LeastSquares/LeastSquares.h"
 
 } // namespace Eigen
 

Eigen/QR

@@ -19,6 +19,9 @@
 namespace Eigen {
 
 /** \defgroup QR_Module QR module
+  *
+  * \nonstableyet
+  *
   * This module mainly provides QR decomposition and an eigen value solver.
   * This module also provides some MatrixBase methods, including:
   *  - MatrixBase::qr(),

Eigen/SVD

@@ -8,6 +8,9 @@
 namespace Eigen {
 
 /** \defgroup SVD_Module SVD module
+  *
+  * \nonstableyet
+  *
   * This module provides SVD decomposition for (currently) real matrices.
   * This decomposition is accessible via the following MatrixBase method:
   *  - MatrixBase::svd()

Eigen/Sparse

@@ -70,6 +70,17 @@
 
 namespace Eigen {
 
+/** \defgroup Sparse_Module Sparse module
+  *
+  * \nonstableyet
+  *
+  * See the \ref TutorialSparse "Sparse tutorial"
+  *
+  * \code
+  * #include <Eigen/QR>
+  * \endcode
+  */
+
 #include "src/Sparse/SparseUtil.h"
 #include "src/Sparse/SparseMatrixBase.h"
 #include "src/Sparse/CompressedStorage.h"

Eigen/src/CMakeLists.txt

@@ -5,6 +5,6 @@ ADD_SUBDIRECTORY(SVD)
 ADD_SUBDIRECTORY(Cholesky)
 ADD_SUBDIRECTORY(Array)
 ADD_SUBDIRECTORY(Geometry)
-ADD_SUBDIRECTORY(Regression)
+ADD_SUBDIRECTORY(LeastSquares)
 ADD_SUBDIRECTORY(Sparse)
 ADD_SUBDIRECTORY(StdVector)

Eigen/src/LU/LU.h

@@ -429,8 +429,7 @@ void LU<MatrixType>::computeKernel(KernelMatrixType *result) const
       .template marked<UpperTriangular>()
       .solveTriangularInPlace(y);
 
-  for(int i = 0; i < m_rank; ++i)
-    result->row(m_q.coeff(i)) = y.row(i);
+  for(int i = 0; i < m_rank; ++i) result->row(m_q.coeff(i)) = y.row(i);
   for(int i = m_rank; i < cols; ++i) result->row(m_q.coeff(i)).setZero();
   for(int k = 0; k < dimker; ++k) result->coeffRef(m_q.coeff(m_rank+k), k) = Scalar(1);
 }

Eigen/src/LeastSquares/CMakeLists.txt

@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_LeastSquares_SRCS "*.h")
+
+INSTALL(FILES 
+  ${Eigen_LeastSquares_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LeastSquares
+  )

Eigen/src/LeastSquares/LeastSquares.h

@@ -22,12 +22,12 @@
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 
-#ifndef EIGEN_REGRESSION_H
-#define EIGEN_REGRESSION_H
+#ifndef EIGEN_LEASTSQUARES_H
+#define EIGEN_LEASTSQUARES_H
 
-/** \ingroup Regression_Module
+/** \ingroup LeastSquares_Module
   *
-  * \regression_module
+  * \leastsquares_module
   *
   * For a set of points, this function tries to express
   * one of the coords as a linear (affine) function of the other coords.
@@ -128,9 +128,9 @@ void linearRegression(int numPoints,
   ei_assert((m.adjoint()*m).lu().solve(v, result));
 }
 
-/** \ingroup Regression_Module
+/** \ingroup LeastSquares_Module
   *
-  * \regression_module
+  * \leastsquares_module
   *
   * This function is quite similar to linearRegression(), so we refer to the
   * documentation of this function and only list here the differences.
@@ -195,4 +195,4 @@ void fitHyperplane(int numPoints,
 }
 
 
-#endif // EIGEN_REGRESSION_H
+#endif // EIGEN_LEASTSQUARES_H

Eigen/src/Regression/CMakeLists.txt

@@ -1,6 +0,0 @@
-FILE(GLOB Eigen_Regression_SRCS "*.h")
-
-INSTALL(FILES 
-  ${Eigen_Regression_SRCS}
-  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Regression
-  )